Overview

Brought to you by YData

Dataset statistics

Number of variables46
Number of observations109656
Missing cells0
Missing cells (%)0.0%
Duplicate rows2764
Duplicate rows (%)2.5%
Total size in memory110.3 MiB
Average record size in memory1.0 KiB

Variable types

Unsupported11
Numeric15
Categorical20

Alerts

Dataset has 2764 (2.5%) duplicate rowsDuplicates
DayOfWeek_AtEntry is highly overall correlated with Is_Weekend and 1 other fieldsHigh correlation
HourOfDay_AtEntry is highly overall correlated with Off_Hours_Entry and 3 other fieldsHigh correlation
InsuranceCategory is highly overall correlated with MMS_Flag and 1 other fieldsHigh correlation
Insurance_Coverage_Ratio is highly overall correlated with Rx_TotalInsPaid and 2 other fieldsHigh correlation
Is_Weekend is highly overall correlated with DayOfWeek_AtEntry and 1 other fieldsHigh correlation
MMS_Flag is highly overall correlated with InsuranceCategory and 2 other fieldsHigh correlation
MMS_Flagged is highly overall correlated with InsuranceCategory and 2 other fieldsHigh correlation
Multiple_Issues is highly overall correlated with MMS_Flag and 1 other fieldsHigh correlation
Off_Hours_Entry is highly overall correlated with HourOfDay_AtEntry and 2 other fieldsHigh correlation
PeakHoursIndicator_AtEntry is highly overall correlated with HourOfDay_AtEntry and 3 other fieldsHigh correlation
Peak_Hours_Rejection is highly overall correlated with HourOfDay_AtEntry and 3 other fieldsHigh correlation
PrescriptionsPerStaff_AtEntry is highly overall correlated with Rx_RxEntered_By and 1 other fieldsHigh correlation
Price_vs_Total_Ratio is highly overall correlated with Rx_Total_PriceHigh correlation
Rx_Days_Supply_Filed is highly overall correlated with Rx_Quantity_FilledHigh correlation
Rx_Quantity_Filled is highly overall correlated with Rx_Days_Supply_FiledHigh correlation
Rx_RxEntered_By is highly overall correlated with Off_Hours_Entry and 5 other fieldsHigh correlation
Rx_TotalInsPaid is highly overall correlated with Insurance_Coverage_Ratio and 1 other fieldsHigh correlation
Rx_TotalRxAmount is highly overall correlated with Insurance_Coverage_Ratio and 1 other fieldsHigh correlation
Rx_Total_Price is highly overall correlated with Price_vs_Total_RatioHigh correlation
StaffVolume_AtEntry is highly overall correlated with PrescriptionsPerStaff_AtEntry and 1 other fieldsHigh correlation
Time_Category is highly overall correlated with HourOfDay_AtEntry and 4 other fieldsHigh correlation
Weekend_Rejection is highly overall correlated with DayOfWeek_AtEntry and 1 other fieldsHigh correlation
Zero_Insurance_Payment is highly overall correlated with Insurance_Coverage_RatioHigh correlation
InsuranceCategory is highly imbalanced (65.0%) Imbalance
MMS_Flag is highly imbalanced (68.2%) Imbalance
Rx_Is_non_Rx? is highly imbalanced (97.1%) Imbalance
Rx_PRN? is highly imbalanced (97.8%) Imbalance
DAW_Required is highly imbalanced (89.1%) Imbalance
MMS_Flagged is highly imbalanced (68.2%) Imbalance
Multiple_Issues is highly imbalanced (76.1%) Imbalance
New_Patient_High_Value is highly imbalanced (88.3%) Imbalance
Rx_Quantity_Filled is highly skewed (γ1 = 24.53137116) Skewed
Rx_Total_Price is highly skewed (γ1 = 91.40968528) Skewed
Rx_TotalInsPaid is highly skewed (γ1 = 32.77645565) Skewed
Rx_TotalRxAmount is highly skewed (γ1 = 91.95428609) Skewed
Price_vs_Total_Ratio is highly skewed (γ1 = -107.6952337) Skewed
Rx RxEntered Date is an unsupported type, check if it needs cleaning or further analysis Unsupported
Insurance_Carrier_Code is an unsupported type, check if it needs cleaning or further analysis Unsupported
Insurance_Accepts_Assignments is an unsupported type, check if it needs cleaning or further analysis Unsupported
Insurance_Billing_Receiver_ID is an unsupported type, check if it needs cleaning or further analysis Unsupported
MainRejectCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
MainRejectDescription is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rx_Status is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rx_Hold_Rx_(Checkbox) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rx_DAW is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rx_Discontinued? is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rx_DUR_Entered_? is an unsupported type, check if it needs cleaning or further analysis Unsupported
DayOfWeek_AtEntry has 15696 (14.3%) zeros Zeros
HourOfDay_AtEntry has 1955 (1.8%) zeros Zeros
Rx_Days_Supply_Filed has 22895 (20.9%) zeros Zeros
Rx_Quantity_Filled has 22915 (20.9%) zeros Zeros
Rx_Total_Price has 1552 (1.4%) zeros Zeros
Rx_TotalInsPaid has 29374 (26.8%) zeros Zeros
Rx_Refills_Remaining has 54019 (49.3%) zeros Zeros
Insurance_Coverage_Ratio has 29374 (26.8%) zeros Zeros
Price_vs_Total_Ratio has 1552 (1.4%) zeros Zeros
PatientTenureDays has 19070 (17.4%) zeros Zeros

Reproduction

Analysis started2025-09-16 22:04:49.786645
Analysis finished2025-09-16 22:05:11.615538
Duration21.83 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Rx RxEntered Date
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size7.2 MiB

DayOfWeek_AtEntry
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6993689
Minimum0
Maximum6
Zeros15696
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:11.647877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8091651
Coefficient of variation (CV)0.67021779
Kurtosis-0.95825845
Mean2.6993689
Median Absolute Deviation (MAD)1
Skewness0.15365031
Sum296002
Variance3.2730783
MonotonicityNot monotonic
2025-09-16T17:05:11.792628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 22548
20.6%
2 18476
16.8%
1 16636
15.2%
0 15696
14.3%
4 15493
14.1%
5 12044
11.0%
6 8763
 
8.0%
ValueCountFrequency (%)
0 15696
14.3%
1 16636
15.2%
2 18476
16.8%
3 22548
20.6%
4 15493
14.1%
5 12044
11.0%
6 8763
 
8.0%
ValueCountFrequency (%)
6 8763
 
8.0%
5 12044
11.0%
4 15493
14.1%
3 22548
20.6%
2 18476
16.8%
1 16636
15.2%
0 15696
14.3%

DayOfMonth_AtEntry
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.217954
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:11.834593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.0062264
Coefficient of variation (CV)0.59181584
Kurtosis-1.3079114
Mean15.217954
Median Absolute Deviation (MAD)9
Skewness0.1057956
Sum1668740
Variance81.112114
MonotonicityNot monotonic
2025-09-16T17:05:11.885466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 13109
 
12.0%
4 5628
 
5.1%
18 3978
 
3.6%
22 3969
 
3.6%
29 3652
 
3.3%
27 3640
 
3.3%
24 3569
 
3.3%
28 3553
 
3.2%
15 3548
 
3.2%
23 3533
 
3.2%
Other values (21) 61477
56.1%
ValueCountFrequency (%)
1 2536
 
2.3%
2 2695
 
2.5%
3 2665
 
2.4%
4 5628
5.1%
5 13109
12.0%
6 2787
 
2.5%
7 2380
 
2.2%
8 2818
 
2.6%
9 2658
 
2.4%
10 3074
 
2.8%
ValueCountFrequency (%)
31 1939
1.8%
30 3385
3.1%
29 3652
3.3%
28 3553
3.2%
27 3640
3.3%
26 2997
2.7%
25 3198
2.9%
24 3569
3.3%
23 3533
3.2%
22 3969
3.6%

HourOfDay_AtEntry
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.442529
Minimum0
Maximum23
Zeros1955
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:11.932353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median11
Q314
95-th percentile17
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.0244052
Coefficient of variation (CV)0.48114829
Kurtosis-0.57923217
Mean10.442529
Median Absolute Deviation (MAD)3
Skewness-0.47314267
Sum1145086
Variance25.244647
MonotonicityNot monotonic
2025-09-16T17:05:11.977908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 17366
15.8%
11 10532
9.6%
10 10187
9.3%
12 10117
9.2%
9 9745
8.9%
15 9583
8.7%
14 9427
8.6%
13 9259
8.4%
16 8876
8.1%
1 4548
 
4.1%
Other values (14) 10016
9.1%
ValueCountFrequency (%)
0 1955
 
1.8%
1 4548
 
4.1%
2 368
 
0.3%
3 17366
15.8%
4 184
 
0.2%
5 295
 
0.3%
6 333
 
0.3%
7 224
 
0.2%
8 678
 
0.6%
9 9745
8.9%
ValueCountFrequency (%)
23 506
 
0.5%
22 408
 
0.4%
21 394
 
0.4%
20 384
 
0.4%
19 520
 
0.5%
18 1079
 
1.0%
17 2688
 
2.5%
16 8876
8.1%
15 9583
8.7%
14 9427
8.6%

PeakHoursIndicator_AtEntry
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
71641 
1
38015 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Length

2025-09-16T17:05:12.024922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:12.060964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring characters

ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

PatientVisitFrequency
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9221292
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:12.095597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum26
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6986936
Coefficient of variation (CV)0.88375618
Kurtosis15.487258
Mean1.9221292
Median Absolute Deviation (MAD)0
Skewness3.3092987
Sum210773
Variance2.8855598
MonotonicityNot monotonic
2025-09-16T17:05:12.144293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 64503
58.8%
2 22311
 
20.3%
3 11621
 
10.6%
4 4128
 
3.8%
5 2192
 
2.0%
6 1707
 
1.6%
7 929
 
0.8%
8 654
 
0.6%
9 532
 
0.5%
10 372
 
0.3%
Other values (16) 707
 
0.6%
ValueCountFrequency (%)
1 64503
58.8%
2 22311
 
20.3%
3 11621
 
10.6%
4 4128
 
3.8%
5 2192
 
2.0%
6 1707
 
1.6%
7 929
 
0.8%
8 654
 
0.6%
9 532
 
0.5%
10 372
 
0.3%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 3
 
< 0.1%
20 3
 
< 0.1%
19 4
< 0.1%
18 6
< 0.1%
17 9
< 0.1%

IsNewPatient
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
92821 
1
16835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92821
84.6%
1 16835
 
15.4%

Length

2025-09-16T17:05:12.190978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:12.219005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 92821
84.6%
1 16835
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 92821
84.6%
1 16835
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 92821
84.6%
1 16835
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 92821
84.6%
1 16835
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 92821
84.6%
1 16835
 
15.4%

Rx_Days_Supply_Filed
Real number (ℝ)

High correlation  Zeros 

Distinct122
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.581902
Minimum0
Maximum365
Zeros22895
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:12.259405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median25
Q330
95-th percentile90
Maximum365
Range365
Interquartile range (IQR)23

Descriptive statistics

Standard deviation30.520824
Coefficient of variation (CV)0.96640235
Kurtosis4.1836461
Mean31.581902
Median Absolute Deviation (MAD)5
Skewness1.4602884
Sum3463145
Variance931.52068
MonotonicityNot monotonic
2025-09-16T17:05:12.320059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 25500
23.3%
25 23226
21.2%
0 22895
20.9%
90 17245
15.7%
28 6412
 
5.8%
1 2280
 
2.1%
10 1371
 
1.3%
7 1368
 
1.2%
5 1322
 
1.2%
15 955
 
0.9%
Other values (112) 7082
 
6.5%
ValueCountFrequency (%)
0 22895
20.9%
1 2280
 
2.1%
2 151
 
0.1%
3 206
 
0.2%
4 143
 
0.1%
5 1322
 
1.2%
6 253
 
0.2%
7 1368
 
1.2%
8 122
 
0.1%
9 44
 
< 0.1%
ValueCountFrequency (%)
365 18
< 0.1%
364 1
 
< 0.1%
360 6
 
< 0.1%
353 1
 
< 0.1%
300 4
 
< 0.1%
270 1
 
< 0.1%
267 1
 
< 0.1%
250 1
 
< 0.1%
240 1
 
< 0.1%
233 1
 
< 0.1%

Rx_Quantity_Filled
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct279
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.017324
Minimum0
Maximum5688
Zeros22915
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:12.377871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q360
95-th percentile120
Maximum5688
Range5688
Interquartile range (IQR)58

Descriptive statistics

Standard deviation92.063867
Coefficient of variation (CV)2.3006003
Kurtosis1020.7469
Mean40.017324
Median Absolute Deviation (MAD)10
Skewness24.531371
Sum4388139.7
Variance8475.7556
MonotonicityNot monotonic
2025-09-16T17:05:12.434054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 23712
21.6%
0 22915
20.9%
30 13341
12.2%
90 13030
11.9%
60 5339
 
4.9%
100 3845
 
3.5%
180 2938
 
2.7%
28 2748
 
2.5%
1 1910
 
1.7%
15 1234
 
1.1%
Other values (269) 18644
17.0%
ValueCountFrequency (%)
0 22915
20.9%
0.2 115
 
0.1%
0.24 2
 
< 0.1%
0.25 305
 
0.3%
0.3 762
 
0.7%
0.4 4
 
< 0.1%
0.42 2
 
< 0.1%
0.5 462
 
0.4%
0.7 4
 
< 0.1%
1 1910
 
1.7%
ValueCountFrequency (%)
5688 1
 
< 0.1%
4000 27
< 0.1%
3784 1
 
< 0.1%
2365 3
 
< 0.1%
1800 1
 
< 0.1%
1620 1
 
< 0.1%
1440 2
 
< 0.1%
1419 1
 
< 0.1%
1350 14
< 0.1%
1182.8 1
 
< 0.1%

InsuranceCategory
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
Private Insurance Carrier
98701 
Public Insurance Carrier
 
7887
Private Patient
 
3068

Length

Max length25
Median length25
Mean length24.648291
Min length15

Characters and Unicode

Total characters2702833
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate Insurance Carrier
2nd rowPublic Insurance Carrier
3rd rowPublic Insurance Carrier
4th rowPublic Insurance Carrier
5th rowPrivate Insurance Carrier

Common Values

ValueCountFrequency (%)
Private Insurance Carrier 98701
90.0%
Public Insurance Carrier 7887
 
7.2%
Private Patient 3068
 
2.8%

Length

2025-09-16T17:05:12.484956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:12.514101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
insurance 106588
32.7%
carrier 106588
32.7%
private 101769
31.2%
public 7887
 
2.4%
patient 3068
 
0.9%

Most occurring characters

ValueCountFrequency (%)
r 528121
19.5%
a 318013
11.8%
e 318013
11.8%
i 219312
8.1%
216244
8.0%
n 216244
8.0%
u 114475
 
4.2%
c 114475
 
4.2%
P 112724
 
4.2%
t 107905
 
4.0%
Other values (6) 437307
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2702833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 528121
19.5%
a 318013
11.8%
e 318013
11.8%
i 219312
8.1%
216244
8.0%
n 216244
8.0%
u 114475
 
4.2%
c 114475
 
4.2%
P 112724
 
4.2%
t 107905
 
4.0%
Other values (6) 437307
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2702833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 528121
19.5%
a 318013
11.8%
e 318013
11.8%
i 219312
8.1%
216244
8.0%
n 216244
8.0%
u 114475
 
4.2%
c 114475
 
4.2%
P 112724
 
4.2%
t 107905
 
4.0%
Other values (6) 437307
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2702833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 528121
19.5%
a 318013
11.8%
e 318013
11.8%
i 219312
8.1%
216244
8.0%
n 216244
8.0%
u 114475
 
4.2%
c 114475
 
4.2%
P 112724
 
4.2%
t 107905
 
4.0%
Other values (6) 437307
16.2%

Insurance_Carrier_Code
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.5 MiB

Insurance_Accepts_Assignments
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.6 MiB

Insurance_Billing_Receiver_ID
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size6.2 MiB

MMS_Flag
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
103323 
1
 
6333

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Length

2025-09-16T17:05:12.556608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:12.583441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Rx_Total_Price
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct9293
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.52945
Minimum-5
Maximum139500
Zeros1552
Zeros (%)1.4%
Negative14
Negative (%)< 0.1%
Memory size856.8 KiB
2025-09-16T17:05:12.622046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile0.08
Q10.16
median7.5
Q340.6475
95-th percentile569.92
Maximum139500
Range139505
Interquartile range (IQR)40.4875

Descriptive statistics

Standard deviation689.67532
Coefficient of variation (CV)5.9696929
Kurtosis15867.971
Mean115.52945
Median Absolute Deviation (MAD)7.42
Skewness91.409685
Sum12668497
Variance475652.04
MonotonicityNot monotonic
2025-09-16T17:05:12.680022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 25339
 
23.1%
0 1552
 
1.4%
10 1135
 
1.0%
400 766
 
0.7%
5 678
 
0.6%
15 562
 
0.5%
12 442
 
0.4%
8 395
 
0.4%
9 393
 
0.4%
255.01 390
 
0.4%
Other values (9283) 78004
71.1%
ValueCountFrequency (%)
-5 3
 
< 0.1%
-1 2
 
< 0.1%
-0.11 9
 
< 0.1%
0 1552
1.4%
0.01 96
 
0.1%
0.02 4
 
< 0.1%
0.03 1
 
< 0.1%
0.04 15
 
< 0.1%
0.05 4
 
< 0.1%
0.06 29
 
< 0.1%
ValueCountFrequency (%)
139500 1
 
< 0.1%
48000 1
 
< 0.1%
33192.6 3
< 0.1%
32785.2 1
 
< 0.1%
26790.76 1
 
< 0.1%
25312.5 1
 
< 0.1%
25200 1
 
< 0.1%
25040 1
 
< 0.1%
22799.39 1
 
< 0.1%
21641.94 2
< 0.1%

Rx_TotalInsPaid
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct9678
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.33582
Minimum-173.04
Maximum48005
Zeros29374
Zeros (%)26.8%
Negative171
Negative (%)0.2%
Memory size856.8 KiB
2025-09-16T17:05:12.738958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-173.04
5-th percentile0
Q10
median23.55
Q394.08
95-th percentile570.83
Maximum48005
Range48178.04
Interquartile range (IQR)94.08

Descriptive statistics

Standard deviation524.59457
Coefficient of variation (CV)3.9343858
Kurtosis1748.3938
Mean133.33582
Median Absolute Deviation (MAD)23.55
Skewness32.776456
Sum14621073
Variance275199.46
MonotonicityNot monotonic
2025-09-16T17:05:12.799361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29374
26.8%
94.08 25353
23.1%
40 1043
 
1.0%
405 725
 
0.7%
0.34 645
 
0.6%
199.93 275
 
0.3%
67.83 223
 
0.2%
112 167
 
0.2%
1.4 150
 
0.1%
10 143
 
0.1%
Other values (9668) 51558
47.0%
ValueCountFrequency (%)
-173.04 1
< 0.1%
-94.18 1
< 0.1%
-80.06 1
< 0.1%
-75.12 1
< 0.1%
-37.02 1
< 0.1%
-33.01 1
< 0.1%
-11.29 1
< 0.1%
-10 1
< 0.1%
-9.95 1
< 0.1%
-9.66 1
< 0.1%
ValueCountFrequency (%)
48005 1
 
< 0.1%
33197.6 2
 
< 0.1%
32790.2 1
 
< 0.1%
26795.76 1
 
< 0.1%
25317.5 1
 
< 0.1%
25205 1
 
< 0.1%
22801.39 1
 
< 0.1%
22133.88 1
 
< 0.1%
21646.94 1
 
< 0.1%
21645 7
< 0.1%

Rx_TotalRxAmount
Real number (ℝ)

High correlation  Skewed 

Distinct10004
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.95787
Minimum0
Maximum139500
Zeros338
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:12.928874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.46
Q17.67
median39.44
Q394.08
95-th percentile593.06
Maximum139500
Range139500
Interquartile range (IQR)86.41

Descriptive statistics

Standard deviation687.62255
Coefficient of variation (CV)4.7436028
Kurtosis16039.19
Mean144.95787
Median Absolute Deviation (MAD)37.64
Skewness91.954286
Sum15895500
Variance472824.78
MonotonicityNot monotonic
2025-09-16T17:05:12.986481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.08 25354
 
23.1%
10 1155
 
1.1%
40 1100
 
1.0%
405 727
 
0.7%
15 685
 
0.6%
5 680
 
0.6%
12 604
 
0.6%
8 404
 
0.4%
250.93 354
 
0.3%
0 338
 
0.3%
Other values (9994) 78255
71.4%
ValueCountFrequency (%)
0 338
0.3%
0.01 82
 
0.1%
0.03 1
 
< 0.1%
0.05 1
 
< 0.1%
0.07 2
 
< 0.1%
0.1 20
 
< 0.1%
0.11 1
 
< 0.1%
0.12 28
 
< 0.1%
0.13 30
 
< 0.1%
0.14 45
 
< 0.1%
ValueCountFrequency (%)
139500 1
< 0.1%
48005 1
< 0.1%
33197.6 2
< 0.1%
33192.6 1
< 0.1%
32790.2 1
< 0.1%
26795.76 1
< 0.1%
25317.5 1
< 0.1%
25205 1
< 0.1%
25040 1
< 0.1%
22801.39 1
< 0.1%

Rx_RxEntered_By
Categorical

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
RS
32168 
MMS
17376 
TS
16835 
MKHAN
8521 
MNK
8249 
Other values (42)
26507 

Length

Max length5
Median length2
Mean length2.5231178
Min length2

Characters and Unicode

Total characters276675
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRS
2nd rowRS
3rd rowRS
4th rowRS
5th rowRS

Common Values

ValueCountFrequency (%)
RS 32168
29.3%
MMS 17376
15.8%
TS 16835
15.4%
MKHAN 8521
 
7.8%
MNK 8249
 
7.5%
FS 6408
 
5.8%
EA 4410
 
4.0%
DP 2277
 
2.1%
UM 2193
 
2.0%
PP 1349
 
1.2%
Other values (37) 9870
 
9.0%

Length

2025-09-16T17:05:13.044820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rs 32168
29.3%
mms 17376
15.8%
ts 16835
15.4%
mkhan 8521
 
7.8%
mnk 8249
 
7.5%
fs 6408
 
5.8%
ea 4410
 
4.0%
dp 2277
 
2.1%
um 2193
 
2.0%
pp 1349
 
1.2%
Other values (37) 9870
 
9.0%

Most occurring characters

ValueCountFrequency (%)
S 74950
27.1%
M 58163
21.0%
R 34768
12.6%
K 18429
 
6.7%
T 17649
 
6.4%
N 17475
 
6.3%
A 15124
 
5.5%
H 11011
 
4.0%
F 6408
 
2.3%
P 5149
 
1.9%
Other values (12) 17549
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 276675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 74950
27.1%
M 58163
21.0%
R 34768
12.6%
K 18429
 
6.7%
T 17649
 
6.4%
N 17475
 
6.3%
A 15124
 
5.5%
H 11011
 
4.0%
F 6408
 
2.3%
P 5149
 
1.9%
Other values (12) 17549
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 276675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 74950
27.1%
M 58163
21.0%
R 34768
12.6%
K 18429
 
6.7%
T 17649
 
6.4%
N 17475
 
6.3%
A 15124
 
5.5%
H 11011
 
4.0%
F 6408
 
2.3%
P 5149
 
1.9%
Other values (12) 17549
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 276675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 74950
27.1%
M 58163
21.0%
R 34768
12.6%
K 18429
 
6.7%
T 17649
 
6.4%
N 17475
 
6.3%
A 15124
 
5.5%
H 11011
 
4.0%
F 6408
 
2.3%
P 5149
 
1.9%
Other values (12) 17549
 
6.3%

PrescriptionsPerStaff_AtEntry
Real number (ℝ)

High correlation 

Distinct173
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.06066
Minimum1
Maximum5501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:13.094707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q120
median37
Q3133
95-th percentile5501
Maximum5501
Range5500
Interquartile range (IQR)113

Descriptive statistics

Standard deviation1189.0199
Coefficient of variation (CV)3.1450506
Kurtosis14.29426
Mean378.06066
Median Absolute Deviation (MAD)23
Skewness3.9945881
Sum41456620
Variance1413768.3
MonotonicityNot monotonic
2025-09-16T17:05:13.149466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5501 5501
 
5.0%
24 2401
 
2.2%
23 1933
 
1.8%
27 1918
 
1.7%
22 1893
 
1.7%
16 1888
 
1.7%
29 1828
 
1.7%
19 1826
 
1.7%
12 1778
 
1.6%
15 1756
 
1.6%
Other values (163) 86934
79.3%
ValueCountFrequency (%)
1 467
 
0.4%
2 760
0.7%
3 849
0.8%
4 1034
0.9%
5 1055
1.0%
6 1350
1.2%
7 1421
1.3%
8 1464
1.3%
9 1315
1.2%
10 1631
1.5%
ValueCountFrequency (%)
5501 5501
5.0%
858 858
 
0.8%
713 713
 
0.7%
702 702
 
0.6%
688 688
 
0.6%
590 590
 
0.5%
579 579
 
0.5%
556 556
 
0.5%
553 553
 
0.5%
502 502
 
0.5%

StaffVolume_AtEntry
Real number (ℝ)

High correlation 

Distinct173
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.63136
Minimum1
Maximum5501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:13.204523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q113
median27
Q386
95-th percentile5501
Maximum5501
Range5500
Interquartile range (IQR)73

Descriptive statistics

Standard deviation1190.4229
Coefficient of variation (CV)3.4743547
Kurtosis14.692087
Mean342.63136
Median Absolute Deviation (MAD)17
Skewness4.0667987
Sum37571584
Variance1417106.6
MonotonicityNot monotonic
2025-09-16T17:05:13.259789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5501 5501
 
5.0%
13 2977
 
2.7%
12 2472
 
2.3%
8 2464
 
2.2%
11 2464
 
2.2%
9 2421
 
2.2%
6 2388
 
2.2%
14 2296
 
2.1%
15 2265
 
2.1%
17 2261
 
2.1%
Other values (163) 82147
74.9%
ValueCountFrequency (%)
1 1070
1.0%
2 1600
1.5%
3 1893
1.7%
4 2028
1.8%
5 2090
1.9%
6 2388
2.2%
7 2184
2.0%
8 2464
2.2%
9 2421
2.2%
10 2050
1.9%
ValueCountFrequency (%)
5501 5501
5.0%
590 590
 
0.5%
553 553
 
0.5%
502 502
 
0.5%
501 501
 
0.5%
473 473
 
0.4%
452 452
 
0.4%
430 430
 
0.4%
383 383
 
0.3%
360 360
 
0.3%

MainRejectCode
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.3 MiB

MainRejectDescription
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size7.5 MiB

Rx_Status
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size6.1 MiB

Rx_Hold_Rx_(Checkbox)
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.8 MiB

Rx_Is_non_Rx?
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
109333 
1
 
323

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 109333
99.7%
1 323
 
0.3%

Length

2025-09-16T17:05:13.309330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:13.335892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 109333
99.7%
1 323
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 109333
99.7%
1 323
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109333
99.7%
1 323
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109333
99.7%
1 323
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109333
99.7%
1 323
 
0.3%

Rx_PRN?
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
109427 
1
 
229

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 109427
99.8%
1 229
 
0.2%

Length

2025-09-16T17:05:13.369716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:13.396183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 109427
99.8%
1 229
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 109427
99.8%
1 229
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109427
99.8%
1 229
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109427
99.8%
1 229
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109427
99.8%
1 229
 
0.2%

Rx_DAW
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size6.1 MiB

Rx_Discontinued?
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.8 MiB

Rx_DUR_Entered_?
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size6.1 MiB

Rx_Refills_Remaining
Real number (ℝ)

Zeros 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7894871
Minimum-2
Maximum99
Zeros54019
Zeros (%)49.3%
Negative14
Negative (%)< 0.1%
Memory size856.8 KiB
2025-09-16T17:05:13.436629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum99
Range101
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.4186178
Coefficient of variation (CV)4.145667
Kurtosis152.39636
Mean1.7894871
Median Absolute Deviation (MAD)1
Skewness12.101591
Sum196228
Variance55.03589
MonotonicityNot monotonic
2025-09-16T17:05:13.500045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 54019
49.3%
3 18404
 
16.8%
2 16003
 
14.6%
1 15681
 
14.3%
4 1343
 
1.2%
5 999
 
0.9%
6 564
 
0.5%
11 490
 
0.4%
10 429
 
0.4%
8 329
 
0.3%
Other values (22) 1395
 
1.3%
ValueCountFrequency (%)
-2 4
 
< 0.1%
-1 10
 
< 0.1%
0 54019
49.3%
1 15681
 
14.3%
2 16003
 
14.6%
3 18404
 
16.8%
4 1343
 
1.2%
5 999
 
0.9%
6 564
 
0.5%
7 315
 
0.3%
ValueCountFrequency (%)
99 263
0.2%
98 110
0.1%
97 55
 
0.1%
96 44
 
< 0.1%
95 43
 
< 0.1%
94 40
 
< 0.1%
93 22
 
< 0.1%
92 16
 
< 0.1%
91 12
 
< 0.1%
90 11
 
< 0.1%

Insurance_Coverage_Ratio
Real number (ℝ)

High correlation  Zeros 

Distinct12822
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69038025
Minimum-5.5192878
Maximum0.99999998
Zeros29374
Zeros (%)26.8%
Negative171
Negative (%)0.2%
Memory size856.8 KiB
2025-09-16T17:05:13.569581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5.5192878
5-th percentile0
Q10
median0.99986074
Q30.99998937
95-th percentile0.999998
Maximum0.99999998
Range6.5192878
Interquartile range (IQR)0.99998937

Descriptive statistics

Standard deviation0.45122639
Coefficient of variation (CV)0.6535911
Kurtosis0.94493662
Mean0.69038025
Median Absolute Deviation (MAD)0.00013750469
Skewness-1.0757892
Sum75704.337
Variance0.20360526
MonotonicityNot monotonic
2025-09-16T17:05:13.637459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29374
26.8%
0.999989371 25353
23.1%
0.9999750006 1043
 
1.0%
0.9999975309 725
 
0.7%
0.796752892 275
 
0.3%
0.9999852575 223
 
0.2%
0.999991072 167
 
0.2%
0.9992862241 149
 
0.1%
0.99990001 131
 
0.1%
0.9999389164 121
 
0.1%
Other values (12812) 52095
47.5%
ValueCountFrequency (%)
-5.519287834 1
 
< 0.1%
-5.15444878 1
 
< 0.1%
-4.745545443 1
 
< 0.1%
-4.460303301 1
 
< 0.1%
-4.064770348 1
 
< 0.1%
-3.137606455 2
 
< 0.1%
-2.932895354 1
 
< 0.1%
-2.83286119 1
 
< 0.1%
-2.740348221 5
< 0.1%
-2.739383847 4
< 0.1%
ValueCountFrequency (%)
0.9999999792 1
 
< 0.1%
0.9999999699 2
 
< 0.1%
0.9999999695 1
 
< 0.1%
0.9999999627 1
 
< 0.1%
0.9999999605 1
 
< 0.1%
0.9999999603 1
 
< 0.1%
0.9999999561 1
 
< 0.1%
0.999999955 1
 
< 0.1%
0.9999999538 1
 
< 0.1%
0.9999999538 7
< 0.1%

Price_vs_Total_Ratio
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct16000
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61568044
Minimum-5000
Maximum4315.5119
Zeros1552
Zeros (%)1.4%
Negative14
Negative (%)< 0.1%
Memory size856.8 KiB
2025-09-16T17:05:13.708427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5000
5-th percentile0.000850331
Q10.074060359
median0.9883806
Q30.99986757
95-th percentile0.99999443
Maximum4315.5119
Range9315.5119
Interquartile range (IQR)0.92580721

Descriptive statistics

Standard deviation29.226124
Coefficient of variation (CV)47.469633
Kurtosis27779.099
Mean0.61568044
Median Absolute Deviation (MAD)0.011610756
Skewness-107.69523
Sum67513.054
Variance854.16635
MonotonicityNot monotonic
2025-09-16T17:05:13.767388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000850331 25323
 
23.1%
0 1552
 
1.4%
0.99990001 1039
 
0.9%
0.9876518823 727
 
0.7%
0.99980004 635
 
0.6%
0.9999333378 525
 
0.5%
0.9999166736 429
 
0.4%
0.9998750156 388
 
0.4%
1.016255465 354
 
0.3%
0.9997500625 291
 
0.3%
Other values (15990) 78393
71.5%
ValueCountFrequency (%)
-5000 3
 
< 0.1%
-0.249937516 2
 
< 0.1%
-0.022490288 9
 
< 0.1%
0 1552
1.4%
5.55521607 × 10-51
 
< 0.1%
6.249570342 × 10-51
 
< 0.1%
9.998900121 × 10-54
 
< 0.1%
0.000111098 1
 
< 0.1%
0.000133315 3
 
< 0.1%
0.000249931 2
 
< 0.1%
ValueCountFrequency (%)
4315.511868 1
< 0.1%
46.76612935 2
< 0.1%
20.87136929 1
< 0.1%
14.85543901 1
< 0.1%
11.69043722 1
< 0.1%
9.573836346 1
< 0.1%
9.12868092 1
< 0.1%
6.989659567 1
< 0.1%
6.165099269 1
< 0.1%
4.567770804 1
< 0.1%

Zero_Insurance_Payment
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
80282 
1
29374 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 80282
73.2%
1 29374
 
26.8%

Length

2025-09-16T17:05:13.818030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:13.845525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 80282
73.2%
1 29374
 
26.8%

Most occurring characters

ValueCountFrequency (%)
0 80282
73.2%
1 29374
 
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80282
73.2%
1 29374
 
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80282
73.2%
1 29374
 
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80282
73.2%
1 29374
 
26.8%

High_Value_Rx
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
82518 
1
27138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 82518
75.3%
1 27138
 
24.7%

Length

2025-09-16T17:05:13.892212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:13.922029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 82518
75.3%
1 27138
 
24.7%

Most occurring characters

ValueCountFrequency (%)
0 82518
75.3%
1 27138
 
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 82518
75.3%
1 27138
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 82518
75.3%
1 27138
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 82518
75.3%
1 27138
 
24.7%

Is_Weekend
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
85197 
1
24459 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Length

2025-09-16T17:05:13.956927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:13.984331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring characters

ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Time_Category
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
Morning
41483 
Afternoon
40912 
Night
25049 
Evening
 
2212

Length

Max length9
Median length7
Mean length7.289323
Min length5

Characters and Unicode

Total characters799318
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowMorning
3rd rowAfternoon
4th rowAfternoon
5th rowMorning

Common Values

ValueCountFrequency (%)
Morning 41483
37.8%
Afternoon 40912
37.3%
Night 25049
22.8%
Evening 2212
 
2.0%

Length

2025-09-16T17:05:14.023891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.058258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
morning 41483
37.8%
afternoon 40912
37.3%
night 25049
22.8%
evening 2212
 
2.0%

Most occurring characters

ValueCountFrequency (%)
n 169214
21.2%
o 123307
15.4%
r 82395
10.3%
i 68744
8.6%
g 68744
8.6%
t 65961
 
8.3%
e 43124
 
5.4%
M 41483
 
5.2%
A 40912
 
5.1%
f 40912
 
5.1%
Other values (4) 54522
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 169214
21.2%
o 123307
15.4%
r 82395
10.3%
i 68744
8.6%
g 68744
8.6%
t 65961
 
8.3%
e 43124
 
5.4%
M 41483
 
5.2%
A 40912
 
5.1%
f 40912
 
5.1%
Other values (4) 54522
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 169214
21.2%
o 123307
15.4%
r 82395
10.3%
i 68744
8.6%
g 68744
8.6%
t 65961
 
8.3%
e 43124
 
5.4%
M 41483
 
5.2%
A 40912
 
5.1%
f 40912
 
5.1%
Other values (4) 54522
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 169214
21.2%
o 123307
15.4%
r 82395
10.3%
i 68744
8.6%
g 68744
8.6%
t 65961
 
8.3%
e 43124
 
5.4%
M 41483
 
5.2%
A 40912
 
5.1%
f 40912
 
5.1%
Other values (4) 54522
 
6.8%

Off_Hours_Entry
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
82171 
1
27485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 82171
74.9%
1 27485
 
25.1%

Length

2025-09-16T17:05:14.101333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.128714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 82171
74.9%
1 27485
 
25.1%

Most occurring characters

ValueCountFrequency (%)
0 82171
74.9%
1 27485
 
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 82171
74.9%
1 27485
 
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 82171
74.9%
1 27485
 
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 82171
74.9%
1 27485
 
25.1%

DAW_Required
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
108066 
1
 
1590

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 108066
98.6%
1 1590
 
1.4%

Length

2025-09-16T17:05:14.163200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.189709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 108066
98.6%
1 1590
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 108066
98.6%
1 1590
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 108066
98.6%
1 1590
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 108066
98.6%
1 1590
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 108066
98.6%
1 1590
 
1.4%

IsRefill
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
64551 
1
45105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 64551
58.9%
1 45105
41.1%

Length

2025-09-16T17:05:14.224306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.251220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 64551
58.9%
1 45105
41.1%

Most occurring characters

ValueCountFrequency (%)
0 64551
58.9%
1 45105
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 64551
58.9%
1 45105
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 64551
58.9%
1 45105
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 64551
58.9%
1 45105
41.1%

PatientTenureDays
Real number (ℝ)

Zeros 

Distinct1973
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466.39661
Minimum0
Maximum2456
Zeros19070
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size856.8 KiB
2025-09-16T17:05:14.294084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q178
median347
Q3752
95-th percentile1366
Maximum2456
Range2456
Interquartile range (IQR)674

Descriptive statistics

Standard deviation450.66673
Coefficient of variation (CV)0.96627359
Kurtosis0.073224389
Mean466.39661
Median Absolute Deviation (MAD)310
Skewness0.92184684
Sum51143187
Variance203100.5
MonotonicityNot monotonic
2025-09-16T17:05:14.429331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19070
 
17.4%
37 1119
 
1.0%
113 763
 
0.7%
103 689
 
0.6%
112 663
 
0.6%
333 607
 
0.6%
111 554
 
0.5%
78 553
 
0.5%
104 448
 
0.4%
109 428
 
0.4%
Other values (1963) 84762
77.3%
ValueCountFrequency (%)
0 19070
17.4%
1 203
 
0.2%
2 123
 
0.1%
3 77
 
0.1%
4 40
 
< 0.1%
5 51
 
< 0.1%
6 53
 
< 0.1%
7 49
 
< 0.1%
8 55
 
0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
2456 1
 
< 0.1%
2276 1
 
< 0.1%
2243 1
 
< 0.1%
2232 3
< 0.1%
2194 3
< 0.1%
2158 2
< 0.1%
2157 1
 
< 0.1%
2155 1
 
< 0.1%
2154 1
 
< 0.1%
2151 3
< 0.1%

MMS_Flagged
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
103323 
1
 
6333

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Length

2025-09-16T17:05:14.481815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.508891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103323
94.2%
1 6333
 
5.8%

Multiple_Issues
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
1
101853 
2
 
7683
3
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 101853
92.9%
2 7683
 
7.0%
3 120
 
0.1%

Length

2025-09-16T17:05:14.542757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.571338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 101853
92.9%
2 7683
 
7.0%
3 120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 101853
92.9%
2 7683
 
7.0%
3 120
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 101853
92.9%
2 7683
 
7.0%
3 120
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 101853
92.9%
2 7683
 
7.0%
3 120
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 101853
92.9%
2 7683
 
7.0%
3 120
 
0.1%

New_Patient_High_Value
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
107922 
1
 
1734

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107922
98.4%
1 1734
 
1.6%

Length

2025-09-16T17:05:14.606201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.633891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 107922
98.4%
1 1734
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 107922
98.4%
1 1734
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 107922
98.4%
1 1734
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 107922
98.4%
1 1734
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 107922
98.4%
1 1734
 
1.6%

Peak_Hours_Rejection
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
71641 
1
38015 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Length

2025-09-16T17:05:14.668765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.696133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring characters

ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 71641
65.3%
1 38015
34.7%

Weekend_Rejection
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
85197 
1
24459 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Length

2025-09-16T17:05:14.732088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.759302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring characters

ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 85197
77.7%
1 24459
 
22.3%

is_anomaly
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
77830 
1
31826 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters109656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77830
71.0%
1 31826
29.0%

Length

2025-09-16T17:05:14.794938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T17:05:14.822005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 77830
71.0%
1 31826
29.0%

Most occurring characters

ValueCountFrequency (%)
0 77830
71.0%
1 31826
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 77830
71.0%
1 31826
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 77830
71.0%
1 31826
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 77830
71.0%
1 31826
29.0%

Interactions

2025-09-16T17:05:09.397878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:57.649727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.468812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.277117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.065679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.993675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.835087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.691492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.520938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.310174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.242391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.047761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.913803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.705997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.518594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.449822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:57.712092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.522737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.329852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.117725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.050410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.887063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.747757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.573940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.438899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.297877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.100075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.965730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.760322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.570208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.504153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:57.767540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.575069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.380124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.257755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.105626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.941967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.804745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.625420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.492847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.351287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.153691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.020520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.815233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.624943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.554952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:57.819662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.628886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.429422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.306974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.159768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.991713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.857573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.677572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.545845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.405287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.205597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.070736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.866073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.675954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.606231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:57.874235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.680774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.479914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.358827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.214746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.044423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.912380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.730609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.600686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.457699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.258692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.123606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.920934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.727526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.662350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:57.929901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.738021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.534102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.459685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.271569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.097908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.971188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.785628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.657502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.512244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.312634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.177280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.976998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.785333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.713665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:57.983775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.791030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.583868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.511194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.325961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.147351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.025195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.836791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.713835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.565023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.364944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.230342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.031005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.835427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.770372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.038933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.846796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.636813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.565619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.384567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.202738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.082407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.892410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.799662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.619687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.419711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.284715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.086648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.975617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.822407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.092393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:58.900505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:04:59.690043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:00.618250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:01.439493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:02.254145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.135962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:03.943478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:04.854773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:05.673890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:06.472725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:07.337781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:08.141523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.025787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T17:05:09.879959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-16T17:05:09.345554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-16T17:05:14.876936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
DAW_RequiredDayOfMonth_AtEntryDayOfWeek_AtEntryHigh_Value_RxHourOfDay_AtEntryInsuranceCategoryInsurance_Coverage_RatioIsNewPatientIsRefillIs_WeekendMMS_FlagMMS_FlaggedMultiple_IssuesNew_Patient_High_ValueOff_Hours_EntryPatientTenureDaysPatientVisitFrequencyPeakHoursIndicator_AtEntryPeak_Hours_RejectionPrescriptionsPerStaff_AtEntryPrice_vs_Total_RatioRx_Days_Supply_FiledRx_Is_non_Rx?Rx_PRN?Rx_Quantity_FilledRx_Refills_RemainingRx_RxEntered_ByRx_TotalInsPaidRx_TotalRxAmountRx_Total_PriceStaffVolume_AtEntryTime_CategoryWeekend_RejectionZero_Insurance_Paymentis_anomaly
DAW_Required1.0000.0210.0090.0280.0430.0170.0090.0270.0060.0000.0090.0090.4910.0020.0400.0170.0070.0070.0070.0340.0000.0210.0000.0200.0100.0370.0680.0110.0020.0020.0240.0420.0000.0120.012
DayOfMonth_AtEntry0.0211.000-0.1130.1120.1060.050-0.0680.3520.2670.1100.0570.0570.0440.0260.1990.1220.0590.1810.181-0.1140.0760.0430.0110.0080.050-0.0560.203-0.053-0.0460.069-0.1220.1270.1100.1330.139
DayOfWeek_AtEntry0.009-0.1131.0000.056-0.0810.0380.1010.1850.1091.0000.0400.0400.0300.0220.192-0.101-0.0020.1420.1420.079-0.106-0.0450.0080.000-0.0560.0830.2000.0890.082-0.0900.1070.1221.0000.0960.070
High_Value_Rx0.0280.1120.0561.0000.1530.0250.2560.1430.0250.0150.0520.0520.0590.2210.0940.1620.0740.0230.0230.1700.0040.0700.0090.0120.0350.0290.1380.0520.0330.0340.1360.0970.0150.2460.085
HourOfDay_AtEntry0.0430.106-0.0810.1531.0000.105-0.1340.3490.2930.1140.1030.1030.0780.0410.9950.096-0.1840.7680.768-0.2170.1550.2190.0100.0040.236-0.1180.416-0.132-0.1100.147-0.3830.9600.1140.2320.160
InsuranceCategory0.0170.0500.0380.0250.1051.0000.1900.1030.1130.0020.6740.6740.4280.0780.1360.0970.0500.0350.0350.0770.0060.1230.0000.0000.0200.0070.1220.0000.0130.0130.0600.0970.0020.2800.043
Insurance_Coverage_Ratio0.009-0.0680.1010.256-0.1340.1901.0000.2100.0220.0150.0770.0770.0520.0220.220-0.303-0.0420.0280.0280.366-0.448-0.0790.0000.000-0.1670.2390.0910.8730.689-0.0460.3630.1260.0150.9520.086
IsNewPatient0.0270.3520.1850.1430.3490.1030.2101.0000.1260.0060.0810.0810.0850.2980.1900.4870.1440.0720.0720.3720.0000.1820.0110.0040.0080.0400.3500.0130.0070.0070.2580.2080.0060.1970.151
IsRefill0.0060.2670.1090.0250.2930.1130.0220.1261.0000.0140.0650.0650.0610.0890.1620.0910.2780.1600.1600.1740.0030.2010.0040.0000.0130.0020.3100.0220.0130.0130.1740.1640.0140.0210.009
Is_Weekend0.0000.1101.0000.0150.1140.0020.0150.0060.0141.0000.0010.0010.0030.0000.0320.0520.0000.0330.0330.1350.0000.0090.0020.0000.0000.0070.1210.0040.0000.0000.1360.0391.0000.0130.016
MMS_Flag0.0090.0570.0400.0520.1030.6740.0770.0810.0650.0011.0001.0000.8950.0140.0950.0830.0390.0210.0210.0810.0000.1180.0060.0000.0260.0030.1440.0020.0000.0000.0630.0950.0010.0580.035
MMS_Flagged0.0090.0570.0400.0520.1030.6740.0770.0810.0650.0011.0001.0000.8950.0140.0950.0830.0390.0210.0210.0810.0000.1180.0060.0000.0260.0030.1440.0020.0000.0000.0630.0950.0010.0580.035
Multiple_Issues0.4910.0440.0300.0590.0780.4280.0520.0850.0610.0030.8950.8951.0000.0150.1030.0570.0210.0220.0220.0620.0000.0840.0060.0080.0170.0130.1000.0180.0090.0090.0470.0720.0030.0580.036
New_Patient_High_Value0.0020.0260.0220.2210.0410.0780.0220.2980.0890.0000.0140.0140.0151.0000.0300.1410.0420.0150.0150.0220.0000.0340.0000.0000.0000.0060.1110.0640.0420.0410.0210.0410.0000.0180.012
Off_Hours_Entry0.0400.1990.1920.0940.9950.1360.2200.1900.1620.0320.0950.0950.1030.0301.0000.2370.0530.3470.3470.4050.0000.2080.0060.0040.0130.0310.8370.0140.0070.0070.4390.9950.0320.2090.113
PatientTenureDays0.0170.122-0.1010.1620.0960.097-0.3030.4870.0910.0520.0830.0830.0570.1410.2371.0000.1410.0590.059-0.3870.4670.1310.0120.0090.151-0.2570.170-0.237-0.2660.428-0.3610.1440.0520.2020.176
PatientVisitFrequency0.0070.059-0.0020.074-0.1840.050-0.0420.1440.2780.0000.0390.0390.0210.0420.0530.1411.0000.1000.1000.0520.059-0.3520.0310.000-0.3620.0100.0590.006-0.027-0.0100.0990.0460.0000.0520.057
PeakHoursIndicator_AtEntry0.0070.1810.1420.0230.7680.0350.0280.0720.1600.0330.0210.0210.0220.0150.3470.0590.1001.0001.0000.1670.0000.0580.0010.0000.0040.0260.5910.0000.0000.0000.2160.6070.0330.0270.018
Peak_Hours_Rejection0.0070.1810.1420.0230.7680.0350.0280.0720.1600.0330.0210.0210.0220.0150.3470.0590.1001.0001.0000.1670.0000.0580.0010.0000.0040.0260.5910.0000.0000.0000.2160.6070.0330.0270.018
PrescriptionsPerStaff_AtEntry0.034-0.1140.0790.170-0.2170.0770.3660.3720.1740.1350.0810.0810.0620.0220.405-0.3870.0520.1670.1671.000-0.499-0.2150.0120.009-0.2440.3300.5980.2860.252-0.4950.9220.3050.1350.1980.167
Price_vs_Total_Ratio0.0000.076-0.1060.0040.1550.006-0.4480.0000.0030.0000.0000.0000.0000.0000.0000.4670.0590.0000.000-0.4991.0000.2110.0000.0000.227-0.3040.046-0.318-0.1460.689-0.4810.0000.0000.0080.000
Rx_Days_Supply_Filed0.0210.043-0.0450.0700.2190.123-0.0790.1820.2010.0090.1180.1180.0840.0340.2080.131-0.3520.0580.058-0.2150.2111.0000.0260.0090.860-0.1260.085-0.082-0.0520.254-0.2500.1190.0090.0900.083
Rx_Is_non_Rx?0.0000.0110.0080.0090.0100.0000.0000.0110.0040.0020.0060.0060.0060.0000.0060.0120.0310.0010.0010.0120.0000.0261.0000.0000.0000.0180.0250.0000.0000.0000.0060.0070.0020.0000.009
Rx_PRN?0.0200.0080.0000.0120.0040.0000.0000.0040.0000.0000.0000.0000.0080.0000.0040.0090.0000.0000.0000.0090.0000.0090.0001.0000.0000.0020.0150.0000.0000.0000.0000.0020.0000.0000.010
Rx_Quantity_Filled0.0100.050-0.0560.0350.2360.020-0.1670.0080.0130.0000.0260.0260.0170.0000.0130.151-0.3620.0040.004-0.2440.2270.8600.0000.0001.000-0.1450.012-0.190-0.1510.231-0.2810.0060.0000.0120.005
Rx_Refills_Remaining0.037-0.0560.0830.029-0.1180.0070.2390.0400.0020.0070.0030.0030.0130.0060.031-0.2570.0100.0260.0260.330-0.304-0.1260.0180.002-0.1451.0000.0760.1930.176-0.2940.3230.0190.0070.0170.066
Rx_RxEntered_By0.0680.2030.2000.1380.4160.1220.0910.3500.3100.1210.1440.1440.1000.1110.8370.1700.0590.5910.5910.5980.0460.0850.0250.0150.0120.0761.0000.0420.0320.0320.5930.5250.1210.2550.166
Rx_TotalInsPaid0.011-0.0530.0890.052-0.1320.0000.8730.0130.0220.0040.0020.0020.0180.0640.014-0.2370.0060.0000.0000.286-0.318-0.0820.0000.000-0.1900.1930.0421.0000.8620.1700.2900.0080.0040.0160.002
Rx_TotalRxAmount0.002-0.0460.0820.033-0.1100.0130.6890.0070.0130.0000.0000.0000.0090.0420.007-0.266-0.0270.0000.0000.252-0.146-0.0520.0000.000-0.1510.1760.0320.8621.0000.3300.2510.0000.0000.0090.005
Rx_Total_Price0.0020.069-0.0900.0340.1470.013-0.0460.0070.0130.0000.0000.0000.0090.0410.0070.428-0.0100.0000.000-0.4950.6890.2540.0000.0000.231-0.2940.0320.1700.3301.000-0.4740.0000.0000.0090.005
StaffVolume_AtEntry0.024-0.1220.1070.136-0.3830.0600.3630.2580.1740.1360.0630.0630.0470.0210.439-0.3610.0990.2160.2160.922-0.481-0.2500.0060.000-0.2810.3230.5930.2900.251-0.4741.0000.3300.1360.1530.127
Time_Category0.0420.1270.1220.0970.9600.0970.1260.2080.1640.0390.0950.0950.0720.0410.9950.1440.0460.6070.6070.3050.0000.1190.0070.0020.0060.0190.5250.0080.0000.0000.3301.0000.0390.2090.116
Weekend_Rejection0.0000.1101.0000.0150.1140.0020.0150.0060.0141.0000.0010.0010.0030.0000.0320.0520.0000.0330.0330.1350.0000.0090.0020.0000.0000.0070.1210.0040.0000.0000.1360.0391.0000.0130.016
Zero_Insurance_Payment0.0120.1330.0960.2460.2320.2800.9520.1970.0210.0130.0580.0580.0580.0180.2090.2020.0520.0270.0270.1980.0080.0900.0000.0000.0120.0170.2550.0160.0090.0090.1530.2090.0131.0000.080
is_anomaly0.0120.1390.0700.0850.1600.0430.0860.1510.0090.0160.0350.0350.0360.0120.1130.1760.0570.0180.0180.1670.0000.0830.0090.0100.0050.0660.1660.0020.0050.0050.1270.1160.0160.0801.000

Missing values

2025-09-16T17:05:10.328803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-16T17:05:10.817025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Rx RxEntered DateDayOfWeek_AtEntryDayOfMonth_AtEntryHourOfDay_AtEntryPeakHoursIndicator_AtEntryPatientVisitFrequencyIsNewPatientRx_Days_Supply_FiledRx_Quantity_FilledInsuranceCategoryInsurance_Carrier_CodeInsurance_Accepts_AssignmentsInsurance_Billing_Receiver_IDMMS_FlagRx_Total_PriceRx_TotalInsPaidRx_TotalRxAmountRx_RxEntered_ByPrescriptionsPerStaff_AtEntryStaffVolume_AtEntryMainRejectCodeMainRejectDescriptionRx_StatusRx_Hold_Rx_(Checkbox)Rx_Is_non_Rx?Rx_PRN?Rx_DAWRx_Discontinued?Rx_DUR_Entered_?Rx_Refills_RemainingInsurance_Coverage_RatioPrice_vs_Total_RatioZero_Insurance_PaymentHigh_Value_RxIs_WeekendTime_CategoryOff_Hours_EntryDAW_RequiredIsRefillPatientTenureDaysMMS_FlaggedMultiple_IssuesNew_Patient_High_ValuePeak_Hours_RejectionWeekend_Rejectionis_anomaly
07/28/2020 8:34:00 PM128200209090.0Private Insurance CarrierMEDDAETYMMISNYDOH02.310.002.81RS271710M/I Patient Gender CodeB000N0N00.0000000.821772100Evening101447010000
12/25/2020 9:37:00 AM12590109090.0Public Insurance CarrierMMS286YMMISNYDOH15.250.005.75RS1212CAInternal CodeB000N0N00.0000000.912885100Morning000153120000
22020-02-04 14:38:0014140209090.0Public Insurance CarrierMMS286YMMISNYDOH11624.681629.681629.68RS7600F000N0N20.9999990.996931010Afternoon000133120001
32020-08-05 15:42:00251501090180.0Public Insurance CarrierMMS286YMMISNYDOH12.090.002.59RS12850Non-Matched PharmacyB000N0N10.0000000.806638100Afternoon000153120000
42/25/2020 9:38:00 AM125902000.0Private Insurance Carrier00002.090.002.59RS121250Non-Matched PharmacyB000N0N20.0000000.806638100Morning001316010000
52020-08-05 15:42:00251501017180.0Public Insurance CarrierMMS286YMMISNYDOH12.680.003.18RS12850Non-Matched PharmacyB000N0N10.0000000.842502100Afternoon000153120000
62/25/2020 9:39:00 AM125902000.0Private Insurance Carrier00002.680.003.18RS121250Non-Matched PharmacyB000N0N20.0000000.842502100Morning001316010000
71/17/2020 11:35:00 AM417111103060.0Private Insurance CarrierAD1YMMISNYDOH0838.68833.68843.68HE66ACInternal CodeFH00N0N10.9881460.994072010Morning00052010101
83/17/2020 10:24:00 AM117100109090.0Private Insurance CarrierHFYMMISNYDOH060.870.0060.87RS9620M/I Compound CodeB000N0N10.0000000.999984110Morning000172010000
92020-10-08 13:18:0038130107530.0Private Insurance CarrierMEDDAETYMMISNYDOH02440.442445.442445.44RS141320M/I Compound CodeF000N0N21.0000000.997955010Afternoon001303010001
Rx RxEntered DateDayOfWeek_AtEntryDayOfMonth_AtEntryHourOfDay_AtEntryPeakHoursIndicator_AtEntryPatientVisitFrequencyIsNewPatientRx_Days_Supply_FiledRx_Quantity_FilledInsuranceCategoryInsurance_Carrier_CodeInsurance_Accepts_AssignmentsInsurance_Billing_Receiver_IDMMS_FlagRx_Total_PriceRx_TotalInsPaidRx_TotalRxAmountRx_RxEntered_ByPrescriptionsPerStaff_AtEntryStaffVolume_AtEntryMainRejectCodeMainRejectDescriptionRx_StatusRx_Hold_Rx_(Checkbox)Rx_Is_non_Rx?Rx_PRN?Rx_DAWRx_Discontinued?Rx_DUR_Entered_?Rx_Refills_RemainingInsurance_Coverage_RatioPrice_vs_Total_RatioZero_Insurance_PaymentHigh_Value_RxIs_WeekendTime_CategoryOff_Hours_EntryDAW_RequiredIsRefillPatientTenureDaysMMS_FlaggedMultiple_IssuesNew_Patient_High_ValuePeak_Hours_RejectionWeekend_Rejectionis_anomaly
10964612/18/2024 2:21:00 PM218140303090.0Private Insurance CarrierBYMMISNYDOH012.8412.8412.84EA413700B000N0N30.9999220.999922000Afternoon0011164010001
10964712/18/2024 4:12:00 PM218160303030.0Private Insurance Carrier11552YMMISNYDOH07.587.757.75EA41400B000N0Y00.9998710.977938000Afternoon001710010000
10964812/20/2024 11:58:00 AM420111101515.0Private Insurance CarrierCAREMARKYMMISNYDOH00.210.210.21EA594110M/I Patient Gender CodeB000N0N00.9952610.995261000Morning000891010100
10964912/23/2024 2:02:00 PM023140103060.0Private PatientCYMMISNYDOH014.500.0014.83EA462900B000N0N00.0000000.977682101Afternoon0001133010011
10965012/23/2024 4:41:00 PM023160102290.0Private PatientCYMMISNYDOH023.500.0024.03EA461750Non-Matched PharmacyB000N0N00.0000000.977904101Afternoon0001018010010
10965112/24/2024 8:55:00 AM12480101245.0Private Insurance CarrierAD1YMMISNYDOH027.000.0027.00FS181750Non-Matched PharmacyB000N0N00.0000000.999963100Morning000596010000
10965212/24/2024 11:07:00 AM1241111110200.0Private Insurance CarrierBBBCSYMMISNYDOH027.120.0027.73FS181700B000N0N10.0000000.977967100Morning0000010101
10965312/27/2024 4:18:00 PM427160103030.0Private Insurance Carrier15581YMMISNYDOH01.240.001.54EA66800B000N0N00.0000000.804672100Afternoon0001414010000
10965412/30/2024 11:23:00 AM030111103030.0Private PatientCYMMISNYDOH019.940.0020.39EA352710M/I Patient Gender CodeB000N0N00.0000000.977882101Morning000348010110
10965512/30/2024 11:23:00 AM030111103030.0Private PatientCYMMISNYDOH012.470.0012.75EA3527CAInternal CodeB000N0N00.0000000.977963101Morning000348010111

Duplicate rows

Most frequently occurring

DayOfWeek_AtEntryDayOfMonth_AtEntryHourOfDay_AtEntryPeakHoursIndicator_AtEntryPatientVisitFrequencyIsNewPatientRx_Days_Supply_FiledRx_Quantity_FilledInsuranceCategoryMMS_FlagRx_Total_PriceRx_TotalInsPaidRx_TotalRxAmountRx_RxEntered_ByPrescriptionsPerStaff_AtEntryStaffVolume_AtEntryRx_Is_non_Rx?Rx_PRN?Rx_Refills_RemainingInsurance_Coverage_RatioPrice_vs_Total_RatioZero_Insurance_PaymentHigh_Value_RxIs_WeekendTime_CategoryOff_Hours_EntryDAW_RequiredIsRefillPatientTenureDaysMMS_FlaggedMultiple_IssuesNew_Patient_High_ValuePeak_Hours_RejectionWeekend_Rejectionis_anomaly# duplicates
1198351011258.0Private Insurance Carrier00.0894.0894.08MNK550155010030.9999890.00085000Night1000010000963
1196351011258.0Private Insurance Carrier00.0894.0894.08MNK550155010020.9999890.00085000Night1000010000885
1211351020258.0Private Insurance Carrier00.0894.0894.08MNK550155010030.9999890.00085000Night1000010000517
1209351020258.0Private Insurance Carrier00.0894.0894.08MNK550155010020.9999890.00085000Night1000010000483
1133350011258.0Private Insurance Carrier00.0894.0894.08MNK550155010030.9999890.00085000Night1000010000312
1130350011258.0Private Insurance Carrier00.0894.0894.08MNK550155010020.9999890.00085000Night1000010000263
1223351030258.0Private Insurance Carrier00.0894.0894.08MNK550155010030.9999890.00085000Night1000010000180
1221351030258.0Private Insurance Carrier00.0894.0894.08MNK550155010020.9999890.00085000Night1000010000179
1219351030258.0Private Insurance Carrier00.0894.0894.08MNK550155010010.9999890.00085000Night1000010000170
1155350020258.0Private Insurance Carrier00.0894.0894.08MNK550155010030.9999890.00085000Night1000010000157

📊 Custom Visualizations & Anomaly Analysis

Generated from Stability_period_Cleaned.xlsx on 2025-09-16 17:05.

🔍 Anomaly Detection Statistics

📈 Standard Feature Distributions

DayOfWeek_AtEntry

DayOfMonth_AtEntry

PatientVisitFrequency

InsuranceCategory

Rx_Status

🚨 Anomaly Analysis

Anomaly Distribution

Anomaly by Day of Week

Anomaly by Hour

Feature Comparison